Overview

Dataset statistics

Number of variables44
Number of observations530
Missing cells8371
Missing cells (%)35.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory182.3 KiB
Average record size in memory352.2 B

Variable types

CAT19
NUM15
UNSUPPORTED4
DATE4
BOOL2

Warnings

Select project has constant value "530" Constant
FR1_ProjectName has constant value "530" Constant
Asset ID: has a high cardinality: 522 distinct values High cardinality
Asset Description has a high cardinality: 527 distinct values High cardinality
Comments has a high cardinality: 177 distinct values High cardinality
longitude is highly correlated with Bund length (m)High correlation
Bund length (m) is highly correlated with longitudeHigh correlation
Other Wood Species is highly correlated with Land Drainage Consent Difficulty and 1 other fieldsHigh correlation
Land Drainage Consent Difficulty is highly correlated with Other Wood SpeciesHigh correlation
Ecological Consent Difficulty is highly correlated with Other Wood SpeciesHigh correlation
Watercourse Type has 23 (4.3%) missing values Missing
Stream Width (m) has 88 (16.6%) missing values Missing
Land Drainage Consent Difficulty has 35 (6.6%) missing values Missing
Ecological Consent Difficulty has 48 (9.1%) missing values Missing
Average member length in Leaky Barrier (m) has 88 (16.6%) missing values Missing
Wood Diameter (cm) has 174 (32.8%) missing values Missing
Height of Leaky Barrier above bed (cm) has 104 (19.6%) missing values Missing
Height of Leaky Barrier above bank (cm) has 281 (53.0%) missing values Missing
Wood Species Used has 415 (78.3%) missing values Missing
Other Wood Species has 466 (87.9%) missing values Missing
Bund height (m) has 521 (98.3%) missing values Missing
Bund width (m) has 521 (98.3%) missing values Missing
Bund length (m) has 521 (98.3%) missing values Missing
Bund Material has 516 (97.4%) missing values Missing
Gully Block Length (m) has 530 (100.0%) missing values Missing
Gully Block Width (m) has 530 (100.0%) missing values Missing
Gully Block Material has 530 (100.0%) missing values Missing
Soil Equipment or Technique Used has 530 (100.0%) missing values Missing
Flood Efficacy has 102 (19.2%) missing values Missing
FR3_AreaRough has 518 (97.7%) missing values Missing
Storage Created (m3) has 8 (1.5%) missing values Missing
FR3_AreaIncreasedLoss has 523 (98.7%) missing values Missing
Changed Flood Pathway? has 138 (26.0%) missing values Missing
Reduced Erosion? has 381 (71.9%) missing values Missing
Asset Condition has 33 (6.2%) missing values Missing
Date Assessed has 32 (6.0%) missing values Missing
Comments has 211 (39.8%) missing values Missing
Creator has 491 (92.6%) missing values Missing
Editor has 12 (2.3%) missing values Missing
Asset ID: is uniformly distributed Uniform
Asset Description is uniformly distributed Uniform
ObjectID has unique values Unique
GlobalID has unique values Unique
CreationDate has unique values Unique
Gully Block Length (m) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Gully Block Width (m) is an unsupported type, check if it needs cleaning or further analysis Unsupported
Gully Block Material is an unsupported type, check if it needs cleaning or further analysis Unsupported
Soil Equipment or Technique Used is an unsupported type, check if it needs cleaning or further analysis Unsupported
Installed Cost (£) has 9 (1.7%) zeros Zeros
Height of Leaky Barrier above bank (cm) has 160 (30.2%) zeros Zeros

Reproduction

Analysis started2020-10-26 09:41:20.406291
Analysis finished2020-10-26 09:43:15.217821
Duration1 minute and 54.81 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

ObjectID
Real number (ℝ≥0)

UNIQUE

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.4320755
Minimum42
Maximum1254
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:15.579291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile175.9
Q1390.5
median684.5
Q3965.75
95-th percentile1187.55
Maximum1254
Range1212
Interquartile range (IQR)575.25

Descriptive statistics

Standard deviation317.9346631
Coefficient of variation (CV)0.4631698816
Kurtosis-1.013738266
Mean686.4320755
Median Absolute Deviation (MAD)287.5
Skewness-0.02965033573
Sum363809
Variance101082.45
MonotocityNot monotonic
2020-10-26T09:43:15.883285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
101410.2%
 
34310.2%
 
32610.2%
 
32710.2%
 
32810.2%
 
32910.2%
 
33010.2%
 
33110.2%
 
33210.2%
 
33410.2%
 
Other values (520)52098.1%
 
ValueCountFrequency (%) 
4210.2%
 
4510.2%
 
4710.2%
 
4810.2%
 
5610.2%
 
ValueCountFrequency (%) 
125410.2%
 
125310.2%
 
125210.2%
 
125110.2%
 
125010.2%
 

GlobalID
Categorical

UNIQUE

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
856a0a2c-4536-44fb-a048-bed5bf74a53f
 
1
bbc3e93a-da63-4ae9-a5f4-f0145999d070
 
1
088c785e-adb1-4307-9321-b9554afcf9be
 
1
0153cc58-adb6-4ad5-bbc5-77808d535f10
 
1
97126ee9-c3a0-4af7-9e4b-a5eb0220d29f
 
1
Other values (525)
525 
ValueCountFrequency (%) 
856a0a2c-4536-44fb-a048-bed5bf74a53f10.2%
 
bbc3e93a-da63-4ae9-a5f4-f0145999d07010.2%
 
088c785e-adb1-4307-9321-b9554afcf9be10.2%
 
0153cc58-adb6-4ad5-bbc5-77808d535f1010.2%
 
97126ee9-c3a0-4af7-9e4b-a5eb0220d29f10.2%
 
1e6995d7-d01d-4f77-9ac2-dd6c91ca2d7910.2%
 
5cbd2645-3c34-4487-8260-05a53865556c10.2%
 
90e03d9a-e6d5-4679-aa58-2eb5e2231cc410.2%
 
8906a1e8-9ce5-487a-b86f-951f060bcefa10.2%
 
6fedb86c-65ad-4fdf-82d7-7c6fa95f615210.2%
 
Other values (520)52098.1%
 
2020-10-26T09:43:16.135034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique530 ?
Unique (%)100.0%
2020-10-26T09:43:16.363420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length36
Median length36
Mean length36
Min length36

Select project
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
A017
530 
ValueCountFrequency (%) 
A017530100.0%
 
2020-10-26T09:43:16.605057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:16.760224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:16.948984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

FR1_ProjectName
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Shipston
530 
ValueCountFrequency (%) 
Shipston530100.0%
 
2020-10-26T09:43:17.183191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:17.352448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:17.469619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length8
Min length8

Asset ID:
Categorical

HIGH CARDINALITY
UNIFORM

Distinct522
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
A017_2007021345
 
2
A017_2006151131
 
2
A017_2006011141
 
2
A017_2005041404
 
2
A017_2004271037
 
2
Other values (517)
520 
ValueCountFrequency (%) 
A017_200702134520.4%
 
A017_200615113120.4%
 
A017_200601114120.4%
 
A017_200504140420.4%
 
A017_200427103720.4%
 
A017_200302134420.4%
 
A017_200213135920.4%
 
A017_200702134320.4%
 
A017_191109144410.2%
 
A017_200427122110.2%
 
Other values (512)51296.6%
 
2020-10-26T09:43:17.732186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique514 ?
Unique (%)97.0%
2020-10-26T09:43:18.002505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length15
Mean length15
Min length15

Asset Type
Categorical

Distinct9
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
leaky_barriers
471 
offline_storage_areas
 
36
runoff_pathway_management
 
14
cross_slope_woodland_creation
 
2
riparian_woodland_creation
 
2
Other values (4)
 
5
ValueCountFrequency (%) 
leaky_barriers47188.9%
 
offline_storage_areas366.8%
 
runoff_pathway_management142.6%
 
cross_slope_woodland_creation20.4%
 
riparian_woodland_creation20.4%
 
floodplain_wetland_restoration20.4%
 
river_restoration10.2%
 
soil_and_land_management10.2%
 
floodplain_woodland_creation10.2%
 
2020-10-26T09:43:18.232029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)0.6%
2020-10-26T09:43:18.422833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:18.657837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length14
Mean length14.97924528
Min length14

Asset Description
Categorical

HIGH CARDINALITY
UNIFORM

Distinct527
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
DELETE
 
4
KNEELONG05LB5
 
1
SUTT5
 
1
CAMNP05LB2
 
1
CAMCH15LB12
 
1
Other values (522)
522 
ValueCountFrequency (%) 
DELETE40.8%
 
KNEELONG05LB510.2%
 
SUTT510.2%
 
CAMNP05LB210.2%
 
CAMCH15LB1210.2%
 
KNEEBEECH02LB110.2%
 
SUTTJERV15LB1510.2%
 
STOURCOW09LB910.2%
 
SUTT210.2%
 
NETHWALT0710.2%
 
Other values (517)51797.5%
 
2020-10-26T09:43:18.956325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique526 ?
Unique (%)99.2%
2020-10-26T09:43:19.256713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length28
Median length13
Mean length12.5754717
Min length1
Distinct114
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2017-05-31 12:00:00
Maximum2020-10-07 11:00:00
2020-10-26T09:43:19.487002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:19.808860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Installed Cost (£)
Real number (ℝ≥0)

ZEROS

Distinct38
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.4509434
Minimum0
Maximum6000
Zeros9
Zeros (%)1.7%
Memory size4.1 KiB
2020-10-26T09:43:20.050910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median100
Q3150
95-th percentile687.5
Maximum6000
Range6000
Interquartile range (IQR)148

Descriptive statistics

Standard deviation535.6539688
Coefficient of variation (CV)2.699175724
Kurtosis52.00653745
Mean198.4509434
Median Absolute Deviation (MAD)50
Skewness6.613252697
Sum105179
Variance286925.1743
MonotocityNot monotonic
2020-10-26T09:43:20.299861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
15011922.5%
 
10011521.7%
 
110219.2%
 
2346.4%
 
120285.3%
 
90234.3%
 
126152.8%
 
180142.6%
 
60101.9%
 
23091.7%
 
Other values (28)6111.5%
 
ValueCountFrequency (%) 
091.7%
 
110219.2%
 
2346.4%
 
1910.2%
 
60101.9%
 
ValueCountFrequency (%) 
600010.2%
 
490010.2%
 
408010.2%
 
400010.2%
 
380010.2%
 

Watercourse Type
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing23
Missing (%)4.3%
Memory size4.1 KiB
ordinary
506 
coast
 
1
ValueCountFrequency (%) 
ordinary50695.5%
 
coast10.2%
 
(Missing)234.3%
 
2020-10-26T09:43:20.566566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)0.2%
2020-10-26T09:43:20.720753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:20.921915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.777358491
Min length3

Stream Width (m)
Real number (ℝ≥0)

MISSING

Distinct23
Distinct (%)5.2%
Missing88
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean1.799773756
Minimum0.4
Maximum38
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:21.123147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.505
Q11
median1.5
Q32
95-th percentile2.485
Maximum38
Range37.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.185874427
Coefficient of variation (CV)1.77015273
Kurtosis67.35301518
Mean1.799773756
Median Absolute Deviation (MAD)0.5
Skewness7.878663072
Sum795.5
Variance10.14979587
MonotocityNot monotonic
2020-10-26T09:43:21.353943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%) 
117332.6%
 
210820.4%
 
1.59017.0%
 
0.5224.2%
 
3112.1%
 
0.75101.9%
 
1.2550.9%
 
1.230.6%
 
0.630.6%
 
2.530.6%
 
Other values (13)142.6%
 
(Missing)8816.6%
 
ValueCountFrequency (%) 
0.410.2%
 
0.5224.2%
 
0.630.6%
 
0.710.2%
 
0.75101.9%
 
ValueCountFrequency (%) 
3810.2%
 
2610.2%
 
2520.4%
 
2410.2%
 
2310.2%
 

Land Drainage Consent Difficulty
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing35
Missing (%)6.6%
Memory size4.1 KiB
easy
453 
moderate
 
21
n_a
 
21
ValueCountFrequency (%) 
easy45385.5%
 
moderate214.0%
 
n_a214.0%
 
(Missing)356.6%
 
2020-10-26T09:43:21.591327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:21.762284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:21.977961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.052830189
Min length3

Ecological Consent Difficulty
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.6%
Missing48
Missing (%)9.1%
Memory size4.1 KiB
n_a
409 
easy
71 
moderate
 
2
ValueCountFrequency (%) 
n_a40977.2%
 
easy7113.4%
 
moderate20.4%
 
(Missing)489.1%
 
2020-10-26T09:43:22.182170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:22.406949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:22.564510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length3
Mean length3.152830189
Min length3

Average member length in Leaky Barrier (m)
Real number (ℝ≥0)

MISSING

Distinct19
Distinct (%)4.3%
Missing88
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean3.730769231
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:22.791521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median3
Q34
95-th percentile6
Maximum30
Range29
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation2.365407128
Coefficient of variation (CV)0.6340266528
Kurtosis50.31163294
Mean3.730769231
Median Absolute Deviation (MAD)1
Skewness5.575694865
Sum1649
Variance5.595150881
MonotocityNot monotonic
2020-10-26T09:43:23.069851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
311621.9%
 
26712.6%
 
2.56512.3%
 
45710.8%
 
6489.1%
 
5377.0%
 
3.5183.4%
 
1.5112.1%
 
871.3%
 
740.8%
 
Other values (9)122.3%
 
(Missing)8816.6%
 
ValueCountFrequency (%) 
110.2%
 
1.5112.1%
 
26712.6%
 
2.56512.3%
 
311621.9%
 
ValueCountFrequency (%) 
3010.2%
 
2510.2%
 
1520.4%
 
1210.2%
 
1020.4%
 

Wood Diameter (cm)
Real number (ℝ≥0)

MISSING

Distinct22
Distinct (%)6.2%
Missing174
Missing (%)32.8%
Infinite0
Infinite (%)0.0%
Mean41.02879213
Minimum1
Maximum3015
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:23.306643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median20
Q325
95-th percentile45
Maximum3015
Range3014
Interquartile range (IQR)10

Descriptive statistics

Standard deviation206.6780566
Coefficient of variation (CV)5.037390715
Kurtosis152.0031941
Mean41.02879213
Median Absolute Deviation (MAD)5
Skewness12.00192965
Sum14606.25
Variance42715.81906
MonotocityNot monotonic
2020-10-26T09:43:23.534577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
159517.9%
 
207914.9%
 
257814.7%
 
30458.5%
 
10214.0%
 
4561.1%
 
3550.9%
 
5050.9%
 
4030.6%
 
10030.6%
 
Other values (12)163.0%
 
(Missing)17432.8%
 
ValueCountFrequency (%) 
110.2%
 
2.2510.2%
 
410.2%
 
7.520.4%
 
10214.0%
 
ValueCountFrequency (%) 
301510.2%
 
204010.2%
 
150110.2%
 
20010.2%
 
10030.6%
 

Height of Leaky Barrier above bed (cm)
Real number (ℝ≥0)

MISSING

Distinct21
Distinct (%)4.9%
Missing104
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean25.83920188
Minimum0
Maximum730
Zeros5
Zeros (%)0.9%
Memory size4.1 KiB
2020-10-26T09:43:23.754786image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q115
median20
Q330
95-th percentile50
Maximum730
Range730
Interquartile range (IQR)15

Descriptive statistics

Standard deviation39.25694349
Coefficient of variation (CV)1.519278485
Kurtosis245.8067708
Mean25.83920188
Median Absolute Deviation (MAD)5
Skewness14.14126967
Sum11007.5
Variance1541.107613
MonotocityNot monotonic
2020-10-26T09:43:24.033717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
1512523.6%
 
207414.0%
 
255610.6%
 
305610.6%
 
10346.4%
 
40163.0%
 
45152.8%
 
60101.9%
 
3561.1%
 
050.9%
 
Other values (11)295.5%
 
(Missing)10419.6%
 
ValueCountFrequency (%) 
050.9%
 
140.8%
 
1.520.4%
 
540.8%
 
710.2%
 
ValueCountFrequency (%) 
73010.2%
 
20010.2%
 
15030.6%
 
10040.8%
 
60101.9%
 

Height of Leaky Barrier above bank (cm)
Real number (ℝ≥0)

MISSING
ZEROS

Distinct22
Distinct (%)8.8%
Missing281
Missing (%)53.0%
Infinite0
Infinite (%)0.0%
Mean79.46646586
Minimum0
Maximum15200
Zeros160
Zeros (%)30.2%
Memory size4.1 KiB
2020-10-26T09:43:24.285813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile100
Maximum15200
Range15200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation964.7526256
Coefficient of variation (CV)12.14037412
Kurtosis246.2319994
Mean79.46646586
Median Absolute Deviation (MAD)0
Skewness15.65335713
Sum19787.15
Variance930747.6287
MonotocityNot monotonic
2020-10-26T09:43:24.493115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
016030.2%
 
15152.8%
 
20132.5%
 
30112.1%
 
50112.1%
 
10091.7%
 
1061.1%
 
6040.8%
 
7530.6%
 
2530.6%
 
Other values (12)142.6%
 
(Missing)28153.0%
 
ValueCountFrequency (%) 
016030.2%
 
0.210.2%
 
0.2510.2%
 
0.510.2%
 
1.210.2%
 
ValueCountFrequency (%) 
1520010.2%
 
105010.2%
 
20010.2%
 
15010.2%
 
11010.2%
 

Wood Species Used
Categorical

MISSING

Distinct6
Distinct (%)5.2%
Missing415
Missing (%)78.3%
Memory size4.1 KiB
other
56 
willow_live
26 
willow_dead
25 
ash
beech
 
1
ValueCountFrequency (%) 
other5610.6%
 
willow_live264.9%
 
willow_dead254.7%
 
ash61.1%
 
beech10.2%
 
softwood10.2%
 
(Missing)41578.3%
 
2020-10-26T09:43:24.735499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)1.7%
2020-10-26T09:43:24.952668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:25.186423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length3.994339623
Min length3

Other Wood Species
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)7.8%
Missing466
Missing (%)87.9%
Memory size4.1 KiB
Machined
42 
Leylandi
15 
Slatted
Machined8
 
1
Machined200
 
1
ValueCountFrequency (%) 
Machined427.9%
 
Leylandi152.8%
 
Slatted50.9%
 
Machined810.2%
 
Machined20010.2%
 
(Missing)46687.9%
 
2020-10-26T09:43:25.422417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique2 ?
Unique (%)3.1%
2020-10-26T09:43:25.613959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:25.812745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length3.601886792
Min length3

Bund height (m)
Real number (ℝ≥0)

MISSING

Distinct5
Distinct (%)55.6%
Missing521
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean1.622222222
Minimum0.6
Maximum2.5
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:26.083613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile0.76
Q11.5
median1.5
Q32
95-th percentile2.3
Maximum2.5
Range1.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5783117191
Coefficient of variation (CV)0.3564935255
Kurtosis-0.02810517702
Mean1.622222222
Median Absolute Deviation (MAD)0.5
Skewness-0.395916204
Sum14.6
Variance0.3344444444
MonotocityNot monotonic
2020-10-26T09:43:26.300661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
230.6%
 
1.530.6%
 
0.610.2%
 
110.2%
 
2.510.2%
 
(Missing)52198.3%
 
ValueCountFrequency (%) 
0.610.2%
 
110.2%
 
1.530.6%
 
230.6%
 
2.510.2%
 
ValueCountFrequency (%) 
2.510.2%
 
230.6%
 
1.530.6%
 
110.2%
 
0.610.2%
 

Bund width (m)
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)66.7%
Missing521
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean9.355555556
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:26.538260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.2
median3
Q33
95-th percentile36
Maximum40
Range39
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation14.77625723
Coefficient of variation (CV)1.579409918
Kurtosis1.527861048
Mean9.355555556
Median Absolute Deviation (MAD)1.8
Skewness1.728529482
Sum84.2
Variance218.3377778
MonotocityNot monotonic
2020-10-26T09:43:26.763444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
330.6%
 
120.4%
 
1.210.2%
 
4010.2%
 
210.2%
 
3010.2%
 
(Missing)52198.3%
 
ValueCountFrequency (%) 
120.4%
 
1.210.2%
 
210.2%
 
330.6%
 
3010.2%
 
ValueCountFrequency (%) 
4010.2%
 
3010.2%
 
330.6%
 
210.2%
 
1.210.2%
 

Bund length (m)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)88.9%
Missing521
Missing (%)98.3%
Infinite0
Infinite (%)0.0%
Mean21.81111111
Minimum1
Maximum75
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:27.013288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median10
Q335.3
95-th percentile67
Maximum75
Range74
Interquartile range (IQR)33.3

Descriptive statistics

Standard deviation27.13293407
Coefficient of variation (CV)1.243995958
Kurtosis0.3152610357
Mean21.81111111
Median Absolute Deviation (MAD)9
Skewness1.257874818
Sum196.3
Variance736.1961111
MonotocityNot monotonic
2020-10-26T09:43:27.260289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
120.4%
 
35.310.2%
 
7510.2%
 
1010.2%
 
510.2%
 
210.2%
 
5510.2%
 
1210.2%
 
(Missing)52198.3%
 
ValueCountFrequency (%) 
120.4%
 
210.2%
 
510.2%
 
1010.2%
 
1210.2%
 
ValueCountFrequency (%) 
7510.2%
 
5510.2%
 
35.310.2%
 
1210.2%
 
1010.2%
 

Bund Material
Categorical

MISSING

Distinct1
Distinct (%)7.1%
Missing516
Missing (%)97.4%
Memory size4.1 KiB
clay
14 
ValueCountFrequency (%) 
clay142.6%
 
(Missing)51697.4%
 
2020-10-26T09:43:27.484559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:27.639863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:27.755348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length3
Mean length3.026415094
Min length3

Gully Block Length (m)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing530
Missing (%)100.0%
Memory size4.3 KiB

Gully Block Width (m)
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing530
Missing (%)100.0%
Memory size4.3 KiB

Gully Block Material
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing530
Missing (%)100.0%
Memory size4.3 KiB

Soil Equipment or Technique Used
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing530
Missing (%)100.0%
Memory size4.3 KiB

Flood Efficacy
Categorical

MISSING

Distinct3
Distinct (%)0.7%
Missing102
Missing (%)19.2%
Memory size4.1 KiB
moderate
325 
high
78 
low
 
25
ValueCountFrequency (%) 
moderate32561.3%
 
high7814.7%
 
low254.7%
 
(Missing)10219.2%
 
2020-10-26T09:43:28.021599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:28.220793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:28.379484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length6.213207547
Min length3

FR3 Area Units
Categorical

Distinct2
Distinct (%)0.4%
Missing1
Missing (%)0.2%
Memory size4.1 KiB
ha
527 
m2
 
2
ValueCountFrequency (%) 
ha52799.4%
 
m220.4%
 
(Missing)10.2%
 
2020-10-26T09:43:29.045513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:29.216308image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:29.336396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length2
Mean length2.001886792
Min length2

FR3_AreaRough
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)66.7%
Missing518
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean42.39166667
Minimum0
Maximum500
Zeros4
Zeros (%)0.8%
Memory size4.1 KiB
2020-10-26T09:43:29.560681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.15
Q31.625
95-th percentile227.09
Maximum500
Range500
Interquartile range (IQR)1.625

Descriptive statistics

Standard deviation144.1139414
Coefficient of variation (CV)3.399581869
Kurtosis11.99796784
Mean42.39166667
Median Absolute Deviation (MAD)0.15
Skewness3.463696761
Sum508.7
Variance20768.82811
MonotocityNot monotonic
2020-10-26T09:43:29.763287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
040.8%
 
0.120.4%
 
0.210.2%
 
3.810.2%
 
50010.2%
 
210.2%
 
1.510.2%
 
110.2%
 
(Missing)51897.7%
 
ValueCountFrequency (%) 
040.8%
 
0.120.4%
 
0.210.2%
 
110.2%
 
1.510.2%
 
ValueCountFrequency (%) 
50010.2%
 
3.810.2%
 
210.2%
 
1.510.2%
 
110.2%
 

Storage Created (m3)
Real number (ℝ≥0)

MISSING

Distinct56
Distinct (%)10.7%
Missing8
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean134.0804598
Minimum0
Maximum8000
Zeros3
Zeros (%)0.6%
Memory size4.1 KiB
2020-10-26T09:43:30.073356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q125
median40
Q387.5
95-th percentile438
Maximum8000
Range8000
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation472.5971503
Coefficient of variation (CV)3.524727996
Kurtosis160.2210438
Mean134.0804598
Median Absolute Deviation (MAD)20
Skewness11.10319328
Sum69990
Variance223348.0665
MonotocityNot monotonic
2020-10-26T09:43:30.334437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
256612.5%
 
306211.7%
 
20489.1%
 
100458.5%
 
40427.9%
 
50397.4%
 
60315.8%
 
150254.7%
 
15254.7%
 
75173.2%
 
Other values (46)12223.0%
 
ValueCountFrequency (%) 
030.6%
 
510.2%
 
10101.9%
 
15254.7%
 
20489.1%
 
ValueCountFrequency (%) 
800010.2%
 
415410.2%
 
234410.2%
 
225010.2%
 
210020.4%
 

FR3_AreaIncreasedLoss
Categorical

MISSING

Distinct2
Distinct (%)28.6%
Missing523
Missing (%)98.7%
Memory size4.1 KiB
0
5
ValueCountFrequency (%) 
061.1%
 
510.2%
 
(Missing)52398.7%
 
2020-10-26T09:43:30.591192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)14.3%
2020-10-26T09:43:30.765919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:30.906670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3
Distinct2
Distinct (%)0.5%
Missing138
Missing (%)26.0%
Memory size4.1 KiB
no
373 
yes
 
19
(Missing)
138 
ValueCountFrequency (%) 
no37370.4%
 
yes193.6%
 
(Missing)13826.0%
 
2020-10-26T09:43:31.085914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Reduced Erosion?
Boolean

MISSING

Distinct2
Distinct (%)1.3%
Missing381
Missing (%)71.9%
Memory size4.1 KiB
yes
85 
no
64 
(Missing)
381 
ValueCountFrequency (%) 
yes8516.0%
 
no6412.1%
 
(Missing)38171.9%
 
2020-10-26T09:43:31.150285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Asset Condition
Categorical

MISSING

Distinct4
Distinct (%)0.8%
Missing33
Missing (%)6.2%
Memory size4.1 KiB
good
426 
moderate
51 
absent
 
11
poor
 
9
ValueCountFrequency (%) 
good42680.4%
 
moderate519.6%
 
absent112.1%
 
poor91.7%
 
(Missing)336.2%
 
2020-10-26T09:43:31.262634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:31.425081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:31.569657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.364150943
Min length3

Date Assessed
Date

MISSING

Distinct97
Distinct (%)19.5%
Missing32
Missing (%)6.0%
Memory size4.1 KiB
Minimum2018-05-31 12:00:00
Maximum2020-10-07 11:00:00
2020-10-26T09:43:31.805313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:32.082509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Comments
Categorical

HIGH CARDINALITY
MISSING

Distinct177
Distinct (%)55.5%
Missing211
Missing (%)39.8%
Memory size4.1 KiB
Slatted
61 
Healey
 
13
Not finished
 
12
Leaky Barrier
 
12
Slatted Dam
 
10
Other values (172)
211 
ValueCountFrequency (%) 
Slatted6111.5%
 
Healey132.5%
 
Not finished122.3%
 
Leaky Barrier 122.3%
 
Slatted Dam 101.9%
 
Clear brash71.3%
 
Leaky barrier 61.1%
 
Needs Banding50.9%
 
Slatted Dam50.9%
 
Vale40.8%
 
Other values (167)18434.7%
 
(Missing)21139.8%
 
2020-10-26T09:43:32.364547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique154 ?
Unique (%)48.3%
2020-10-26T09:43:32.701554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length112
Median length7
Mean length12.22264151
Min length3

CreationDate
Date

UNIQUE

Distinct530
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2019-08-03 18:37:53
Maximum2020-10-07 15:13:58
2020-10-26T09:43:32.941109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:33.480761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Creator
Categorical

MISSING

Distinct1
Distinct (%)2.6%
Missing491
Missing (%)92.6%
Memory size4.1 KiB
gsmithadmin
39 
ValueCountFrequency (%) 
gsmithadmin397.4%
 
(Missing)49192.6%
 
2020-10-26T09:43:33.735266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:33.897427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:34.011242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length3.588679245
Min length3
Distinct529
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
Minimum2020-01-23 19:44:47
Maximum2020-10-15 08:50:40
2020-10-26T09:43:34.232359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:34.504825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Editor
Categorical

MISSING

Distinct1
Distinct (%)0.2%
Missing12
Missing (%)2.3%
Memory size4.1 KiB
gsmithadmin
518 
ValueCountFrequency (%) 
gsmithadmin51897.7%
 
(Missing)122.3%
 
2020-10-26T09:43:34.800261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-26T09:43:34.960117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:35.061398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length11
Mean length10.81886792
Min length3

longitude
Real number (ℝ)

HIGH CORRELATION

Distinct528
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.631711497
Minimum-1.81663
Maximum-1.484981474
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:35.293654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.81663
5-th percentile-1.769682917
Q1-1.709705417
median-1.641680582
Q3-1.542998485
95-th percentile-1.499103574
Maximum-1.484981474
Range0.3316485259
Interquartile range (IQR)0.1667069318

Descriptive statistics

Standard deviation0.09211958168
Coefficient of variation (CV)-0.05645580231
Kurtosis-1.321759833
Mean-1.631711497
Median Absolute Deviation (MAD)0.08304808244
Skewness-0.08702664033
Sum-864.8070932
Variance0.008486017329
MonotocityNot monotonic
2020-10-26T09:43:35.604784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.57799333320.4%
 
-1.7021120.4%
 
-1.49940176510.2%
 
-1.75613978310.2%
 
-1.57762333310.2%
 
-1.76974666710.2%
 
-1.70132341710.2%
 
-1.75638645210.2%
 
-1.669441410.2%
 
-1.52622981910.2%
 
Other values (518)51897.7%
 
ValueCountFrequency (%) 
-1.8166310.2%
 
-1.8165610.2%
 
-1.8105310.2%
 
-1.7998310.2%
 
-1.78321032110.2%
 
ValueCountFrequency (%) 
-1.48498147410.2%
 
-1.48524288210.2%
 
-1.48540450210.2%
 
-1.48565578310.2%
 
-1.48577447610.2%
 

latitude
Real number (ℝ≥0)

Distinct526
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.03504679
Minimum51.97943431
Maximum52.07702522
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:35.962131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum51.97943431
5-th percentile51.99026192
Q152.02048027
median52.03628758
Q352.05212133
95-th percentile52.07092871
Maximum52.07702522
Range0.09759090455
Interquartile range (IQR)0.03164105907

Descriptive statistics

Standard deviation0.02390600746
Coefficient of variation (CV)0.0004594212735
Kurtosis-0.4130484614
Mean52.03504679
Median Absolute Deviation (MAD)0.0158276673
Skewness-0.4723106848
Sum27578.5748
Variance0.0005714971927
MonotocityNot monotonic
2020-10-26T09:43:36.257759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
51.9902833330.6%
 
51.9801120.4%
 
51.9902316720.4%
 
52.0507822310.2%
 
52.0173837510.2%
 
52.0363006310.2%
 
52.02997210.2%
 
52.0556162810.2%
 
52.03583310.2%
 
52.040610.2%
 
Other values (516)51697.4%
 
ValueCountFrequency (%) 
51.9794343110.2%
 
51.9795416710.2%
 
51.9797866710.2%
 
51.9800283310.2%
 
51.9801120.4%
 
ValueCountFrequency (%) 
52.0770252210.2%
 
52.0765680710.2%
 
52.076365510.2%
 
52.0760242510.2%
 
52.0757770510.2%
 

log10_price
Real number (ℝ)

Distinct38
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.572885388
Minimum-2
Maximum3.778151974
Zeros0
Zeros (%)0.0%
Memory size4.1 KiB
2020-10-26T09:43:36.558362image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile0.004321373783
Q10.3031960574
median2.000043427
Q32.176120211
95-th percentile2.829869242
Maximum3.778151974
Range5.778151974
Interquartile range (IQR)1.872924154

Descriptive statistics

Standard deviation1.071204284
Coefficient of variation (CV)0.6810440816
Kurtosis0.5523428699
Mean1.572885388
Median Absolute Deviation (MAD)0.1760767838
Skewness-1.036673032
Sum833.6292555
Variance1.147478619
MonotocityNot monotonic
2020-10-26T09:43:36.794787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%) 
2.17612021111922.5%
 
2.00004342711521.7%
 
0.00432137378310219.2%
 
0.3031960574346.4%
 
2.079217436285.3%
 
1.954290762234.3%
 
2.100405012152.8%
 
2.255296632142.6%
 
1.778223627101.9%
 
-291.7%
 
Other values (28)6111.5%
 
ValueCountFrequency (%) 
-291.7%
 
0.00432137378310219.2%
 
0.3031960574346.4%
 
1.27898211710.2%
 
1.778223627101.9%
 
ValueCountFrequency (%) 
3.77815197410.2%
 
3.69019696610.2%
 
3.61066122810.2%
 
3.60206107710.2%
 
3.57978473910.2%
 

Interactions

2020-10-26T09:41:37.418225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:38.305928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:38.704494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:39.165292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:39.674686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:40.158473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:40.622385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:41.063489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:41.509140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:41.966069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:42.448954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:42.906225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:43.313052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:43.727528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:44.166955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:44.583737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:45.053113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:45.525836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:46.031969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:46.473577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:46.867429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:47.348910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:47.822091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:48.309884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:48.759682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:49.103020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:49.374216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:49.665195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:50.028534image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:50.354519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:50.700684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:51.050579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:51.417194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:51.786094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:52.165988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:52.545782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:52.845808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:53.180818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:53.521365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:53.820221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:54.271363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:54.773085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:55.725061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:56.316945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:56.936143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:57.507529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:58.113824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:58.711555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:59.319675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:41:59.963055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:00.571536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:01.183035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:01.785908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:02.388910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:03.066445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:03.695416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:04.499871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:05.449276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:06.478681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:07.251737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:08.008063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:08.614514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:09.206722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:09.803039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:10.500577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:10.928867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:11.397206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:11.779899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:12.132207image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:12.539932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:13.050558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:13.577392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:14.058172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:14.498871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:15.012202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:15.505643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:15.864070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:16.251163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:16.830472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:17.379179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:17.803329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:18.203911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:18.631687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:19.069624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:19.501788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:19.905497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:20.373826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:20.741587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:21.147049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:21.565269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:21.984614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:22.422692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:22.836402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:23.691698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:24.237668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:24.870687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:25.518925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:26.156112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:26.766008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:27.395276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:28.007218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:28.600364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:29.091134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:29.531339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:30.024632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:30.534799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:30.831655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:31.140325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:31.415478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:31.825963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:32.155251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:32.428021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:32.714715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:33.058766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:33.504086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:33.770116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:34.046283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:34.329689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:34.642039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:34.999007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:35.292336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:35.548499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:35.796620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:36.210427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:36.671634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:36.979579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:37.221651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:37.506288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:37.876593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:38.173414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:38.420785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:38.650931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:38.905518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:39.262917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:39.587426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:39.830513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:40.086259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:40.348265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:40.677835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:41.043710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:41.290958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:41.558256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:41.809337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:42.131843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:42.499745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:42.762862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:42.999878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:43.237150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:43.557277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:44.143218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:47.657598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:47.850991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:48.049678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:48.624916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:49.010285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:50.083255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-26T09:42:51.720989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:51.906420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:52.097614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:52.748392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:53.780142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:55.085912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:55.778159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:56.365753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:57.076422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:57.754412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:58.550117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:59.019818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:59.330603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:42:59.825447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:00.178858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:00.753053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:01.376646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:01.765223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:02.050766image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:02.274057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:02.746232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:03.130543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:03.331188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:03.925099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:04.458551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:04.792080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:05.099340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:05.424658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:05.699688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:05.836327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:05.991871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:06.125157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:06.264348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:06.395380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:06.656979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:06.871407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:07.162003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:07.470537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:07.671537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-26T09:43:37.031661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-26T09:43:37.557537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-26T09:43:38.158296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-26T09:43:38.691701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-26T09:43:39.233633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-26T09:43:08.278117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:11.795565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:12.988223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-26T09:43:14.566171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

ObjectIDGlobalIDSelect projectFR1_ProjectNameAsset ID:Asset TypeAsset DescriptionDate InstalledInstalled Cost (£)Watercourse TypeStream Width (m)Land Drainage Consent DifficultyEcological Consent DifficultyAverage member length in Leaky Barrier (m)Wood Diameter (cm)Height of Leaky Barrier above bed (cm)Height of Leaky Barrier above bank (cm)Wood Species UsedOther Wood SpeciesBund height (m)Bund width (m)Bund length (m)Bund MaterialGully Block Length (m)Gully Block Width (m)Gully Block MaterialSoil Equipment or Technique UsedFlood EfficacyFR3 Area UnitsFR3_AreaRoughStorage Created (m3)FR3_AreaIncreasedLossChanged Flood Pathway?Reduced Erosion?Asset ConditionDate AssessedCommentsCreationDateCreatorEditDateEditorlongitudelatitudelog10_price
088333059012-1c4d-43c7-adb7-496972f3c9b4A017ShipstonA017_2006151117leaky_barriers92020-06-02 11:00:001ordinary3.0easyn_a5.020.030.00.0willow_deadNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN135.0NaNnoNaNgood2020-06-15 11:00:00NaN2020-06-15 10:18:01gsmithadmin2020-08-03 11:44:52NaN-1.71507852.0697160.004321
1106602b90a36-d984-4b36-a299-4fbbe884f7b2A017ShipstonA017_2008051630leaky_barriersBLOCKPOTT*12020-08-05 11:00:00260ordinary1.5easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN40.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:24NaN2020-09-18 19:01:54gsmithadmin-1.73287152.0262322.414990
210676f0e9956-8d95-4ebf-a540-9f0cabc735e9A017ShipstonA017_2008051627leaky_barriersBLOCKPOTT*22020-08-05 11:00:00260ordinary1.5easyn_a3.5NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN30.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:36NaN2020-09-18 19:02:03gsmithadmin-1.73257452.0263122.414990
31068a5d7435c-363e-41bf-ac2e-cea617acbfcdA017ShipstonA017_2008051625leaky_barriersBLOCKPOTT*32020-08-05 11:00:00260ordinary1.5easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN25.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:44:49NaN2020-09-18 19:02:11gsmithadmin-1.73245052.0263992.414990
4106901f0b340-5a4d-483d-91d0-7161b87d3ccbA017ShipstonA017_2008051622leaky_barriersBLOCKPOTT*42020-08-05 11:00:00260ordinary2.0easyn_a4.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN30.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 16:45:02NaN2020-09-18 19:02:50gsmithadmin-1.73244252.0264922.414990
510587b72fa58-62c9-4c1c-9617-a3fb8bf7cd83A017ShipstonA017_2008051617leaky_barriersBLOCKPOTT*52020-08-05 11:00:00260ordinary1.0easyn_a3.0NaN15.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhaNaN25.0NaNnoNaNgood2020-06-29 23:00:00Slatted2020-08-05 15:22:21NaN2020-09-18 19:02:22gsmithadmin-1.73228152.0265922.414990
64421d32eb4f-29cb-4dd9-8e9c-4d94d643dc02A017ShipstonA017_2002241950leaky_barriersBLOCKPOTT01LB12017-10-10 12:00:0060ordinary1.0easyn_a2.510.05.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Low priority2020-02-24 20:06:31gsmithadmin2020-02-28 07:24:22gsmithadmin-1.73184652.0229101.778224
744306539413-6f5b-4643-8dca-6e8940039a05A017ShipstonA017_2002242006leaky_barriersBLOCKPOTT02LB22017-10-10 12:00:0060ordinary1.0easyn_a2.010.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNNaNNaNmoderate2020-02-15 12:00:00Silted, some scouring. Low Priority2020-02-24 20:13:57gsmithadmin2020-02-28 07:24:32gsmithadmin-1.73191352.0230801.778224
84442e6b604a-fca5-44f1-85e1-118e97dd4afdA017ShipstonA017_2002242017leaky_barriersBLOCKPOTT03LB32017-10-10 12:00:0060ordinary1.5easyn_a2.515.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Silted. Low priority2020-02-24 20:21:28gsmithadmin2020-02-28 07:24:41gsmithadmin-1.73183852.0232611.778224
94453824078b-cb41-40f2-8e84-469b90340bcbA017ShipstonA017_2002242021leaky_barriersBLOCKPOTT04LB42017-10-10 12:00:0060ordinaryNaNeasyn_aNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlowhaNaN10.0NaNnoNaNmoderate2020-02-15 12:00:00Badly silted. Low priority2020-02-24 20:25:54gsmithadmin2020-02-28 07:24:51gsmithadmin-1.73185752.0234051.778224

Last rows

ObjectIDGlobalIDSelect projectFR1_ProjectNameAsset ID:Asset TypeAsset DescriptionDate InstalledInstalled Cost (£)Watercourse TypeStream Width (m)Land Drainage Consent DifficultyEcological Consent DifficultyAverage member length in Leaky Barrier (m)Wood Diameter (cm)Height of Leaky Barrier above bed (cm)Height of Leaky Barrier above bank (cm)Wood Species UsedOther Wood SpeciesBund height (m)Bund width (m)Bund length (m)Bund MaterialGully Block Length (m)Gully Block Width (m)Gully Block MaterialSoil Equipment or Technique UsedFlood EfficacyFR3 Area UnitsFR3_AreaRoughStorage Created (m3)FR3_AreaIncreasedLossChanged Flood Pathway?Reduced Erosion?Asset ConditionDate AssessedCommentsCreationDateCreatorEditDateEditorlongitudelatitudelog10_price
52010030e4e6703-8d5e-4d6c-af5e-7e68182a9ac1A017ShipstonA017_2007161459leaky_barriersSUTTWALT13LB122020-06-01 11:00:00150ordinary1.0easyn_a2.520.020.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN25.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:18:47NaN2020-07-20 19:22:18gsmithadmin-1.52759552.0555072.176120
5211002a6eff560-2b31-4a28-b5a3-1513759fef80A017ShipstonA017_2007161502leaky_barriersSUTTWALT14LB132020-06-01 11:00:00150ordinary1.0easyn_a2.012.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN20.0NaNnoNaNgood2020-07-15 23:00:00Leaky barrier2020-07-16 16:18:40NaN2020-07-20 19:31:42gsmithadmin-1.52775252.0555252.176120
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52310009396b320-6d61-44cb-aaf6-a041de0e2cf0A017ShipstonA017_2007161508leaky_barriersSUTTWALT16LB152020-06-01 11:00:00150ordinary1.5easyn_a3.015.025.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN15.0NaNnoNaNgood2020-07-15 23:00:00Leaky barrier2020-07-16 16:18:26NaN2020-07-20 19:31:05gsmithadmin-1.52822552.0556152.176120
52499933dd10d5-ad0b-415d-ac97-b0639f88beddA017ShipstonA017_2007161511leaky_barriersSUTTWALT17LB162020-06-01 11:00:00150ordinary1.0easyn_a2.020.020.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN15.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:18:18NaN2020-07-20 19:34:33gsmithadmin-1.52843652.0556162.176120
5259980171abab-a48d-4d82-a873-740b8160b978A017ShipstonA017_2007161517leaky_barriersSUTTWALT18LB172020-07-16 11:00:00150ordinary1.0easyn_a2.015.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN15.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:18:12NaN2020-07-20 19:36:26gsmithadmin-1.52867952.0557112.176120
526997b4c83c49-7077-415a-9693-9560b522dac5A017ShipstonA017_2007161520leaky_barriersSUTTWALT19LB182020-07-16 11:00:00150ordinary1.5easyn_a2.515.015.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN20.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:18:05NaN2020-07-20 19:38:59gsmithadmin-1.52880952.0558722.176120
527996daf06754-06c8-4a34-a25d-99bb95c352c4A017ShipstonA017_2007161523leaky_barriersSUTTWALT20LB192020-07-16 11:00:001ordinaryNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNhaNaN20.0NaNNaNNaNgoodNaTLeaky Barrier2020-07-16 16:17:58NaN2020-09-03 20:42:20gsmithadmin-1.52900752.0559930.004321
528995c560682c-3e19-4a58-9505-935067403815A017ShipstonA017_2007161526leaky_barriersSUTTWALT21LB202020-06-01 11:00:00150ordinary1.5easyn_a2.515.020.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN20.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:17:49NaN2020-07-20 19:42:36gsmithadmin-1.52902652.0561422.176120
52999476e4cf1b-14bf-415a-a918-048b221e8462A017ShipstonA017_2007161530leaky_barriersSUTTWALT22LB212020-06-01 11:00:00150ordinary1.5easyn_a2.520.020.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNmoderatehaNaN20.0NaNnoNaNgood2020-07-15 23:00:00Leaky Barrier2020-07-16 16:17:42NaN2020-07-20 19:45:17gsmithadmin-1.52913452.0562402.176120